Kaiming He et al[1]} first proposed the residual block structure and solved some of the problems caused by deep neural networks by introducing a skip connection and combination. The heterogeneous kernel-based residual block is shown in detail in Figure REF . The relevant formula is analyzed as follows:
\(\mathbf {y}_{i}=h\left(\mathbf {x}_{i}\right)+\mathcal {F}\left(\mathbf {x}_{i}, \mathcal {W}_{i}\right)\) 
\(\mathbf {x}_{i+1}=\mathcal {F}\left(\mathbf {x}_{i}, \mathcal {W}_{i}\right)+h\left(\mathbf {x}_{i}\right)\) 
